LearningHumanoidWalking

Code for the papers:

Learning Bipedal Walking On Planned Footsteps For Humanoid Robots (Humanoids2022)
Rohan P. Singh, Mehdi Benallegue, Mitsuharu Morisawa, Rafael Cisneros, Fumio Kanehiro
Learning Bipedal Walking for Humanoids with Current Feedback (arxiv)
Rohan P. Singh, Zhaoming Xie, Pierre Gergondet, Fumio Kanehiro
(WIP on branch topic/omnidirectional-walk)

Code structure:

A rough outline for the repository that might be useful for adding your own robot:

LearningHumanoidWalking/
├── envs/                <-- Actions and observation space, PD gains, simulation step, control decimation, init, ...
├── tasks/               <-- Reward function, termination conditions, and more...
├── rl/                  <-- Code for PPO, actor/critic networks, observation normalization process...
├── models/              <-- MuJoCo model files: XMLs/meshes/textures
├── trained/             <-- Contains pretrained model for JVRC
└── scripts/             <-- Utility scripts, etc.

Requirements:

Python version: 3.7.11
Pytorch
pip install:
- mujoco==2.2.0
- mujoco-python-viewer
- ray==1.9.2
- transforms3d
- matplotlib
- scipy

Usage:

Environment names supported:

Task Description	Environment name
Basic Walking Task	'jvrc_walk'
Stepping Task (using footsteps)	'jvrc_step'

To train:

$ python run_experiment.py train --logdir <path_to_exp_dir> --num_procs <num_of_cpu_procs> --env <name_of_environment>

To play:

We need to write a script specific to each environment.
For example, debug_stepper.py can be used with the jvrc_step environment.

$ PYTHONPATH=.:$PYTHONPATH python scripts/debug_stepper.py --path <path_to_exp_dir>

What you should see:

Ascending stairs:

Descending stairs:

Walking on curves:

Citation

If you find this work useful in your own research:

@inproceedings{singh2022learning,
  title={Learning Bipedal Walking On Planned Footsteps For Humanoid Robots},
  author={Singh, Rohan P and Benallegue, Mehdi and Morisawa, Mitsuharu and Cisneros, Rafael and Kanehiro, Fumio},
  booktitle={2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids)},
  pages={686--693},
  year={2022},
  organization={IEEE}
}

Baichenjia/LearningHumanoidWalking